The identification and separation of one or more objects within an image by existing techniques may involve segmentation, whereby the digital image is partitioned into multiple groups of pixels or voxels that share the same characteristics and hence into image segments. Segmentation is used in image analysis in many if not all areas, including medicine, multimedia, robotics, assembly line and public surveillance.
There are many methods of image segmentation (including thresholding, clustering, edge detection, region-growing and split-and-merge techniques). While they are all very useful, more often than not no single method is sufficient to accurately separate or segment two or more objects within an image. A combination of methods is hence often required, but even then the combination is often insufficient to enable separation of two adjacent object. Segmentation accuracy determines the eventual success or failure of computerized analysis procedures. However, segmentation of non-trivial images is one of the most difficult tasks in image analysis. The accuracy is always deteriorated by noises, and becomes even more challenging when the gaps between different objects are very small.